# Use Cases

Memgraph Zero solves concrete problems that teams face when data is scattered
across systems, silos, and compliance boundaries. Each use case below shows a
real pattern, explains the architecture, and provides a working example you can
run yourself.

## [Federated GQL Across Heterogeneous Backends](https://memgraph.com/docs/memgraph-zero/memgql/use-cases/federated-gql)

Run graph queries across non-graph stores like ClickHouse and PostgreSQL
through a single GQL endpoint. Tables become nodes and edges through a mapping
file; cross-store joins, pattern matching, and variable-length paths work
without ETL or schema changes.

## [Public-Private Data / Data Sovereignty](https://memgraph.com/docs/memgraph-zero/memgql/use-cases/public-private)

Keep sensitive data sovereign while querying it alongside public knowledge
graphs. A GDPR-compliant PostgreSQL database stores private customer profiles,
while a public Memgraph instance hosts the open product catalog. MemGQL joins
  both from a single query endpoint without ever moving the regulated data.

## [Horizontally Scalable Distributed Compute](https://memgraph.com/docs/memgraph-zero/memgql/use-cases/distributed)

Spread graph computation across multiple nodes for workloads that exceed the
capacity of a single instance. MemGQL can partition queries and materialize
intermediate results across a cluster.

## [Enterprise Context Sharing](https://memgraph.com/docs/memgraph-zero/memgql/use-cases/enterprise-context)

Share canonical context — org charts, product hierarchies, ontologies — across
departments without forcing every team into the same database. Each team keeps
its own data store; MemGQL provides a unified graph view.

## [Agentic Access](https://memgraph.com/docs/memgraph-zero/memgql/use-cases/agentic)

Give AI agents a single semantic layer to discover and query any data in the
organization. Agents use standard GQL to explore relationships across
relational, graph, and lakehouse backends without needing to know where each
dataset lives.